In performing analysis of various aspects of an enterprise (e.g., a business, an educational organization, or a government agency), data can be received in the form of a time series, which is a collection of data points over time. Based on the received time series data, analysts can perform forecasting by applying forecasting algorithms on the time series data to produce future forecast data points. A widely used forecasting algorithm is the Holt-Winters forecasting algorithm, also known as the triple exponential smoothing algorithm.
An issue associated with using forecasting algorithms, such as the Holt-Winters algorithm, is that there has to be a certain number of data points in a time series before the forecasting algorithm can be applied. However, in many cases, a time series having the proper length is not available to enable application of the forecasting algorithm. In one example, historical sales data for products manufactured by a company may not have been collected and stored on time. In such situations, many conventional forecasting algorithms cannot be employed to perform forecasting.